基于Gabor感知多成份字典的图像稀疏表示算法研究

Sparse Representations of Images by a Multi-component Gabor Perception Dictionary

如何设计合适的能够匹配各层面几何结构的图像稀疏表示过完备字典,进而形成对图像的稀疏分解是当前研究者关注的热点问题.根据图像的几何结构特性,从人类视觉系统特性出发,建立了匹配各层面图像结构的Gabor感知多成份字典,进而提出一种高效的基于匹配追踪的图像稀疏分解算法.实验结果表明:Gabor感知多成份字典具有对图像中平滑、边缘与纹理结构的自适应性,与Anisotropic refinement-Gaussian(AR-Gauss)混合字典相比以较少的原子实现了对图像更为高效的稀疏分解.

It is currently a hot research topic that how to design an effective over-complete dictionary matching various geometric structures of images to provide sparse representation of images. A multi-component Gabor perception dictionary matching various image structures is constructed in terms of geometric properties of the local structures and the perception character of HVS. Furthermore, an effective algorithm based on the matching pursuit method is proposed to obtain sparse decomposition of images with our dictionary. The experimental results indicate that the Gabor multi-component perception dictionary can adaptively provide a precise and complete characterization of local geometry structures, such as plain, edge and texture in images. In comparison with the anisotropic refinement-Gaussian （AR-Gauss） mixed dictionary, our dictionary has a much sparser representation of images.